{1531} revision 3 modified: 02-18-2021 19:48 gmt

PMID-24204224 The Convallis rule for unsupervised learning in cortical networks 2013 - Pierre Yger  1 , Kenneth D Harris

This paper aims to unify and reconcile experimental evidence of in-vivo learning rules with  established STDP rules.  In particular, the STDP rule fails to accurately predict change in strength in response to spike triplets, e.g. pre-post-pre or post-pre-post.  Their model instead involves the competition between two time-constant threshold circuits / coincidence detectors, one which controls LTD and another LTP, and is such an extension of the classical BCM rule.  (BCM: inputs below a threshold will weaken a synapse; those above it will strengthen. )

They derive the model from optimization criteria that neurons should try to optimize the skewedness of the distribution of their membrane potential: much time spent either firing spikes or strongly inhibited.  This maps to a objective function F that looks like a valley - hence the 'convallis' in the name (latin for valley); the objective is differentiated to yield a weighting function for weight changes; they also add a shrinkage function (line + heaviside function) to gate weight changes 'off' at resting membrane potential. 

A network of firing neurons successfully groups correlated rate-encoded inputs, better than the STDP rule.  it can also cluster auditory inputs of spoken digits converted into cochleogram.  But this all seems relatively toy-like: of course algorithms can associate inputs that co-occur.  The same result was found for a recurrent balanced E-I network with the same cochleogram, and convalis performed better than STDP.   Meh.

Perhaps the biggest thing I got from the paper was how poorly STDP fares with spike triplets:

Pre following post does not 'necessarily' cause LTD; it's more complicated than that, and more consistent with the two different-timeconstant coincidence detectors.  This is satisfying as it allows for apical dendritic depolarization to serve as a contextual binding signal - without negatively impacting the associated synaptic weights.